Cognitive impairments are a disabling characteristic of mental health disorders, most severely impacting individuals with schizophrenia, bipolar disorder, and major depression. The cognitive deficits experienced by these populations are associated with impairments in daily functioning, making cognition a critical target for treatment and intervention. Despite this need for treatment, it remains unclear whether the cognitive impairments experienced by patients with schizophrenia, bipolar disorder, and depression have different etiologies requiring the development of distinct interventions, or whether similar treatments would be effective for all three clinical groups. Therefore, a better understanding of how these deficits are associated with neurobiological abnormalities across diagnostic groups is an important next step in efforts to intervene on and remediate cognitive impairment. The current project directly addresses this step by assessing differences in brain network organization across diagnostic groups, and testing hypotheses about how those differences relate to cognitive ability. Research has shown that the organization and integrity of functional brain networks supports cognitive functions such as memory, executive functioning, and processing speed. Therefore, we hypothesize that the organization of those networks could provide critical insights into neurobiological abnormalities associated with mental illness. The first aim of the study will measure the efficiency of functional networks believed to support higher-order cognition, the fronto-parietal network (FPN) and the cingulo- opercular network (CON), in individuals with schizophrenia, bipolar disorder, depression, and healthy controls. Differences in network efficiency, calculated using graph theoretic algorithms, will be used to predict performance on tasks of working memory, episodic memory, executive function, and processing speed, within and across diagnostic group. Additionally, the second aim of the study will look at specific brain regions known as hubs, which are particularly important for transmittin information between networks. The degree to which a hub node communicates with other networks will be used to predict cognitive performance as well, to gain a better sense of whether reductions in the participation of specific brain regions influences cognitive performance. We predict that these relationships will hold both within and across diagnostic group, and that no interactions with group will be observed, based on the hypothesis that relationships between cognition and network metrics are similar across diagnostic groups, but that the magnitude of those metrics differs in a graded fashion (controls>depression>bipolar>schizophrenia). Through these two aims, we can gain a more complex understanding of how functional brain network organization is associated with cognitive deficits, and also whether these relationships differ depending on one's diagnostic status. With this information, more targeted predictions can be made regarding the specificity of abnormalities across mental health disorders and therefore the types of treatments necessary for addressing cognitive impairments in these clinical populations.